BUSINESSINTELLIGENCEUnderstanding Oracle BI Components and Repository Modeling Basics

by Abhinav Banerjee

INTRODUCTION

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The importance of Business Intelligence (BI) is rising by the day. BI systems, which help organizations make better and more informed decisions, are becoming crucial for success.

One of the most common reasons for unsuccessful or delayed BI implementations is an improperly modeled repository not adhering to basic dimensional modeling principles. This article discusses this subject and describes the intricacies related to repository modeling and the associated concepts.

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IntroductionIn an Oracle Business Intelligence (OBI) implementation, the repository plays the most important role as the heart of any BI environment. The entire BI implementation can go wrong because of a repository that is not well designed. Repository (RPD) designing and modeling is one of the most complex processes in an OBI implementation. RPD success is dependant on knowledge of a few principles, which include dimensional modeling and data modeling.

In any implementation, we need to ensure our data and dimensional models are well-designed because the model plays such a significant role. Once these data and

dimensional models are in place, we need to ensure that the physical and the business models are also properly designed and developed based on the operational or analytical reporting requirements.

Dimensional models are level-based measures, aggregates, multiple facts, multiple logical sources, conforming dimensions, slowly changing dimensions designed to optimize performance for process reporting. This is a different approach from traditional data-relational models which are optimized to process transactions. The complexity of dimensional modeling increases when requirements call for the inclusion of very large data volumes.

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Table of ContentsCHAPTER 1Dimensional Modeling7 Dimensional Modeling (DM)8 Star Schema9 Snowflake Schema

CHAPTER 2Oracle BI Architecture11 Oracle BI Architecture14 Oracle BI Server14 Oracle BI Presentation Services15 Oracle BI Repository15 Actions Services16 Security Service17 Cluster Controller Severs17 Oracle BI Administration Tool

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CHAPTER 3The Oracle BI Development Cycle19 Oracle BI Development Cycle

CHAPTER 4Building the Oracle BI Model21 Build Oracle BI Model22 Import Objects24 Build Physical Model27 Physical Layer Best Practices28 Build BMM Model34 BMM Layer Best Practices38 Build the Presentation Layer40 Presentation Layer Best Practices

About AuthorAbhinav BanerjeeAbhinav Banerjee is a Consulting Manager working with KPI Partners. He has more than eight years of business intelligence and data integration experience with more than four years in OBIEE (custom and packaged analytics). He has worked with several global clients in various domains that include telecommunications, high tech, manufacturing, energy, education, and oil and gas. He is also a frequent speaker at various Oracle conferences such as COLLABORATE and Oracle OpenWorld. Abhinav specializes in OBIA as well as custom OBIEE implementations. He can be reached at abhinav.banerjee@kpipartners.com.

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KPI Partners is an Oracle Partner who specializes in Oracle Business Intelligence (BI) and Oracle Enterprise Performance Management so-lutions. The award-winning staff at KPI Partners comes directly from the product engineering departments at Oracle, Siebel, and Hyperion. In addition to consulting services, KPI Partners offers training, support, and exclusive pre-packaged analytic solution extensions for Oracle Business Intelligence.

KPI Partners works with both corporate technology departments and corporate business units to develop value-added business intelligence solutions, not just new technology deployments.

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About KPI

CHAPTER 1

DIMENSIONAL MODELING

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Dimensional Modeling (DM)DM refers to the methodology used to design data warehouses optimized for performance while querying and reporting. DM uses the concept of facts and dimensions.

Facts, or measures, refer to measurable items or numeric values. These include sales quantity, sales amount, time taken, etc.

Dimensions are the descriptors, or the relative terms, for the measures. Facts are relative to the dimensions. Some of the most common dimensions include account, customer, product, and date.

Dimensional modeling requires the design of star or snowflake schema.

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Star SchemaThe star schema architecture constitutes a central fact table with multiple dimension tables surrounding it. It will have one-to-many relationships between the dimensions and the fact table. The dimensions typically have the relative descriptive attributes that describe business entities.

In case of a star schema, no two dimensions will be joined directly; rather, all the joins between the dimensions will be through the central fact table. Joins are completed via a foreign key relationship, with the dimension having the primary key and the fact having the foreign keys to join to the dimension.

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Fact Table

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Snowflake SchemaThe snowflake schema architecture also has a central fact table with multiple dimension tables and one to many relationships between the dimension and the fact table, but it also will have one-to-many relationships between dimensions. The dimensions are further normalized into multiple related tables. In this case, multiple dimension tables will exist related to the main dimension table.

Normally, we have one-to-many relationships between the dimensions. A primary key-foreign key relationship exists between the dimension and the fact tables as well as between dimensions.

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Fact Table

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CHAPTER 2

ORACLE BI ARCHITECTURE

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